Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.
In France, the national health insurance (NHI) covers the majority of health expenses, the rest being more or less covered by the complementary health insurance (CHI). But do insured persons really know their CHI contract? This empirical work explores administrative data matched with survey data providing information on usually unobserved characteristics (health status or risk aversion). First, we identify the factors that influence policyholders’ knowledge of their contract, then we analyze the gap between reimbursements and contributions using quantile regressions. Paradoxically, this gap does not seem to be widened by the consumption of so-called “comfort” care but rather by hospital care. Moreover, a better knowledge of one’s contract seems to be beneficial.
Mexican cities along the US-Mexico border, especially Cd. Juarez, became notorious due to high femicide rates supposedly associated with maquiladora industries and the NAFTA. Nonetheless, statistical evaluation of data from 1990 to 2012 shows that their rates are consistent with other Mexican cities’ rates and tend to fall with increased employment opportunities in maquiladoras. Femicide rates in Cd. Juarez are in most years like rates in Cd. Chihuahua and Ensenada and, as a share of overall homicide rates, are lower than in most cities evaluated. These results challenge conventional wisdom and most of the literature on the subject.
In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector machines, neural networks and also direct multi-output approaches. We survey the subject and illustrate the use of ten such approaches using simulations and a real use case. In our simulations, the underlying model is linear in the explanatory factors for one response and nonlinear for the others. We focus on the advantages and disadvantages of the different approaches and show how their hyperparameters may be tuned. Our simulations show that even when the underlying relation between the response and the explanatory variables is linear, the RSM approach is outperformed by the direct neural network multivariate model, for any sample size (<50) and much more for very small samples (15 or 20). When the underlying relation is nonlinear, the RSM approach is outperformed by most of the machine learning approaches for small samples (n ≤ 30).
The popular view is that governments should crack down on tax avoidance by multinational corporations, but in practice, lax anti-profit-shifting policies are common. Here, we analyze how controlling profit shifting influences fiscal competition. Equilibrium tax rates are determined by the elasticities of two components: retained profit and capital mobility. Anti-profit-shifting policies decrease the elasticity of the first, but increase the elasticity of the second. The impact of these policies on equilibrium tax rates is then ambiguous. We show that there are cases in which laxer policies increase equilibrium tax rates and countries’ well-being by favoring investments. We use estimates of different elasticities to show that our model can support lax enforcement.
The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using a non-parametric approach. We opted for a Bayesian approach using Bernstein polynomials which provides confidence intervals, tests and a simple way to compare two na-GICs. The methodology is applied to examine wage dynamics in a US university with a particular attention devoted to unbundling and anti-discrimination policies. Our findings are the detection of wage scale compression for higher quantiles for all academics and an apparent pro-female wage increase compared to males. But this pro-female policy works only for academics and not for the para-academics categories created by the unbundling policy.
This paper highlights how technology can contribute to reaching the 2015 Paris Agreement goals of net zero carbon dioxide (CO2) emissions and global warming below 2°C in 2100. It uses the Advanced Climate Change Long-term model (ACCL), particularly adapted to quantify the consequences of energy price and technology shocks on CO2 emissions, temperature, climate damage and Gross Domestic Product (GDP). The simulations show that without climate policies the warming may be +5°C in 2100, with considerable climate damage. An acceleration in ‘usual’ technical progress not targeted at reducing CO2- even worsens global warming and climate damage. According to our estimates, the world does not achieve climate goals in 2100 without ‘green’ technologies. Intervening only via energy prices, e.g. a carbon tax, requires challenging hypotheses of international coordination and price increase for polluting energies. We assess a multi-lever climate strategy combining energy efficiency gains, carbon sequestration, and a decrease of 3% per year in the relative price of ‘clean’ electricity with a 1 to 1.5% annual rise in the relative price of polluting energy sources. None of these components alone is sufficient to reach climate objectives. Our last and most important finding is that our composite scenario achieves the climate goals.
This paper investigates how affective forecasting errors (A.F.E.s), the difference between anticipated emotion and the emotion actually experienced, may induce changes in preferences on time, risk and occupation after combat. Building on psychological theories incorporating the role of emotion in decision-making, we designed a before-and-after-mission survey for Danish soldiers deployed to Afghanistan in 2011. Our hypothesis of an effect from A.F.E.s is tested by controlling for other mechanisms that may also change preferences: immediate emotion, trauma effect – proxied by post-traumatic stress disorder (P.T.S.D.) – and changes in wealth and risk perception. At the aggregate level, results show stable preferences before and after mission. We find positive A.F.E.s for all three emotions studied (fear, anxiety and excitement), with anticipated emotions stronger than those actually experienced. We provide evidence that positive A.F.E.s regarding fear significantly increase risk tolerance and impatience, while positive A.F.E.s regarding excitement strengthen the will to stay in the military. Trauma has no impact on these preferences.
We study how firm premia influence the gender wage gap for 21 European countries. We use a quadrennial harmonized matched employer–employee dataset to estimate gender-specific firm premia. Subsequently, we decompose the firm-specific wage premia differential into within- and between-firm components. On average, the former accounts mainly for the decline in the pay gap between 2002 and 2014. We pay particular attention to the development of each component by age group, and find that the between-firm component is associated with an increase in the gender pay gap over age. The decomposition of firm premia allows us to investigate how institutional settings relate to each component. We associate the within-firm component with collective bargaining at the national and firm levels, and the between-firm component with family policies. Decentralized wage bargaining is associated with a larger within-firm pay gap, whereas family policies incentivizing women to return to employment after family formation are linked to a smaller between-firm component.
This paper studies government spending multipliers in a panel of OECD countries. While recent literature has highlighted the differences in government consumption and investment effects, we extend this approach sectorally and report findings that suggest strong heterogeneities across sectors for government spending and output. Differences in price stickiness and sectors’ position in the production network are the main drivers of these heterogeneities.